Rapid Communication
Copyright ©2008 The WJG Press and Baishideng. All rights reserved.
World J Gastroenterol. Jan 28, 2008; 14(4): 563-568
Published online Jan 28, 2008. doi: 10.3748/wjg.14.563
Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis
Edith Lahner, Marco Intraligi, Massimo Buscema, Marco Centanni, Lucy Vannella, Enzo Grossi, Bruno Annibale
Edith Lahner, Lucy Vannella, Bruno Annibale, Department of Digestive and Liver Disease, University “La Sapienza”, 2nd Medical School, Ospedale Sant’Andrea, Rome, Italy
Marco Intraligi, Massimo Buscema, Semeion Research Center, Rome Italy
Marco Centanni, Endocrinology Unit, Department of Experimental Medicine and Pathology, University “La Sapienza”, Polo Pontino, Latina, Italy
Enzo Grossi, Bracco Imaging Spa, Milan, Italy
Correspondence to: Bruno Annibale, MD, Department of Digestive and Liver Disease, University Sapienza, Ospedale Sant’Andrea, Via di Grottarossa 1035, Roma 00189, Italy. bruno.annibale@uniroma1.it
Telephone: +39-6-49972369
Fax: +39-6-4455292
Received: May 17, 2007
Revised: November 8, 2007
Published online: January 28, 2008

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients.

METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease.

RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS.

CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.

Keywords: Atrophic body gastritis, Thyroid disease, Artificial neural networks